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This study explores innovative approaches to improve the accuracy of AI responses, focusing on groundedness and self-promting techniques. Through a comprehensive comparative analysis of methods and approaches from leading large language model companies, Open AI and Google, we examine the effectiveness of these methods in reducing errors and improving the reliability of AI outputs. The study uses inductive and comparative methodologies to evaluate existing strategies and propose new solutions. Our results show significant improvements in the accuracy of AI responses through the synergistic application of groundedness and self-promting, offering valuable insights for AI developers and researchers. The study contributes to the ongoing efforts to improve the reliability and accuracy of AI systems across various applications by addressing critical issues in the evolving AI development landscape. The broader expected implications of this study will enable future authors in similar fields to develop detailed plans to expand and improve the market vision for combating hallucinations and improving the reliability of AI models in the long term.

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Enhancing Factual Consistency in Large Language Models: An Integrative Paradigm of Grounding and Self-Prompting Methods for Hallucination Minimization

  • 会议论文

  • 发表日期：

    2026年

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  • Said Gulyamov,
  • Islambek Rustambekov,
  • Sirio Zolea,
  • Dilshod Egamberdiev,
  • Ekaterina Kan,
  • Madina Yakubova,
  • Andrey Rodionov,
  • Gayrat Eshboyev,
  • Timur Pulatov

摘要

This study explores innovative approaches to improve the accuracy of AI responses, focusing on groundedness and self-promting techniques. Through a comprehensive comparative analysis of methods and approaches from leading large language model companies, Open AI and Google, we examine the effectiveness of these methods in reducing errors and improving the reliability of AI outputs. The study uses inductive and comparative methodologies to evaluate existing strategies and propose new solutions. Our results show significant improvements in the accuracy of AI responses through the synergistic application of groundedness and self-promting, offering valuable insights for AI developers and researchers. The study contributes to the ongoing efforts to improve the reliability and accuracy of AI systems across various applications by addressing critical issues in the evolving AI development landscape. The broader expected implications of this study will enable future authors in similar fields to develop detailed plans to expand and improve the market vision for combating hallucinations and improving the reliability of AI models in the long term.